#ifndef CAFFE_SWISH_LAYER_HPP_
#define CAFFE_SWISH_LAYER_HPP_

#include <vector>
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/layers/neuron_layer.hpp"
#include "caffe/layers/sigmoid_layer.hpp"


namespace caffe {

/* @brief Swish non-linearity @f$ y = x \sigma (\beta x) @f$. A novel activation function that tends to work better than ReLU [1].
 * [1] Prajit Ramachandran, Barret Zoph, Quoc V. Le. "Searching for Activation Functions". arXiv preprint arXiv:1710.05941v2 (2017). */
template <typename Dtype>
class SwishLayer : public NeuronLayer<Dtype> {
 public:
  /* @param param provides SwishParameter swish_param, with SwishLayer options:
   *   - beta (\b optional, default 1). the value @f$ \beta @f$ in the @f$ y = x \sigma (\beta x) @f$. */
  explicit SwishLayer(const LayerParameter& param) : NeuronLayer<Dtype>(param), sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
                                                     sigmoid_input_(new Blob<Dtype>()), sigmoid_output_(new Blob<Dtype>()) {}
  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
  virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
  virtual inline const char* type() const { return "Swish"; }

 protected:
  /* @param bottom input Blob vector (length 1) -# @f$ (N \times C \times H \times W) @f$ the inputs @f$ x @f$
   * @param top output Blob vector (length 1)
   *   -# @f$ (N \times C \times H \times W) @f$ the computed outputs @f$ y = x \sigma (\beta x) @f$. */
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);

  /* @brief Computes the error gradient w.r.t. the sigmoid inputs.
   * @param top output Blob vector (length 1), providing the error gradient with respect to the outputs
   *   -# @f$ (N \times C \times H \times W) @f$
   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$ with respect to computed outputs @f$ y @f$
   * @param propagate_down see Layer::Backward.
   * @param bottom input Blob vector (length 1)
   *   -# @f$ (N \times C \times H \times W) @f$ the inputs @f$ x @f$; Backward fills their diff with
   *      gradients @f$ \frac{\partial E}{\partial x} = \frac{\partial E}{\partial y}(\beta y + \sigma (\beta x)(1 - \beta y))
   *      @f$ if propagate_down[0] */
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);

  /// The internal SigmoidLayer
  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
  /// sigmoid_input_ stores the input of the SigmoidLayer.
  shared_ptr<Blob<Dtype> > sigmoid_input_;
  /// sigmoid_output_ stores the output of the SigmoidLayer.
  shared_ptr<Blob<Dtype> > sigmoid_output_;
  /// bottom vector holder to call the underlying SigmoidLayer::Forward
  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
  /// top vector holder to call the underlying SigmoidLayer::Forward
  vector<Blob<Dtype>*> sigmoid_top_vec_;
};

}  // namespace caffe
#endif  // CAFFE_SWISH_LAYER_HPP_
